Stanford Energy is brought to you by the Precourt Institute for Energy
By Jeff McMahon
Wind farms have traditionally made less money for the electricity they produce because they have been unable to predict how windy it will be tomorrow.
“The way a lot of power markets work is you have to schedule your assets a day ahead,” said Michael Terrell, the head of energy market strategy at Google. “And you tend to get compensated higher when you do that than if you sell into the market real-time.
“Well, how do variable assets like wind schedule a day ahead when you don't know the wind is going to blow?” Terrell asked, “and how can you actually reserve your place in line?”
“We're not getting the full benefit and the full value of that power.”
Here’s how: Google and the Google-owned Artificial Intelligence firm DeepMind combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central United States. Using machine learning, they have been able to better predict wind production, better predict electricity supply and demand, and as a result, reduce operating costs.
“What we've been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that's available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets,” Terrell said in a recent seminar hosted by the Stanford Precourt Institute of Energy. Stanford University posted video of the seminar last week. The result has been a 20 percent increase in revenue for wind farms, Terrell said.
The Department of Energy listed improved wind forecasting as a first priority in its 2015 Wind Vision report, largely to improve reliability: “Improve Wind Resource Characterization,” the report said at the top of its list of goals. “Collect data and develop models to improve wind forecasting at multiple temporal scales—e.g., minutes, hours, days, months, years.”
Google’s goal has been more sweeping: to scrub carbon entirely from its energy portfolio, which consumes as much power as two San Franciscos.
Google achieved an initial milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell said. But the company has not been carbon-free in every location at every hour, which is now its new goal—what Terrell calls its “24x7 carbon-free” goal.
“We're really starting to turn our efforts in this direction, and we're finding that it's not something that's easy to do. It's arguably a moon shot, especially in places where the renewable resources of today are not as cost effective as they are in other places.”
The scientists at London-based DeepMind have demonstrated that artificial intelligence can help by increasing the market viability of renewables at Google and beyond.
“Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide,” said DeepMind program manager Sims Witherspoon and Google software engineer Carl Elkin. In a Deepmind blog post, they outline how they boosted profits for Google’s wind farms in the Southwest Power Pool, an energy market that stretches across the plains from the Canadian border to north Texas:
“Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind-power output 36 hours ahead of actual generation. Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance.” The DeepMind system predicts wind-power output 36 hours in advance, allowing power producers to make more lucrative advance bids to supply power to the grid.